Unknown

Dataset Information

0

Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling.


ABSTRACT: The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single-cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell-dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high-quality scRNA-seq data.

SUBMITTER: Sun B 

PROVIDER: S-EPMC8262260 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC11293576 | biostudies-literature
| S-EPMC8682753 | biostudies-literature
| S-EPMC8179149 | biostudies-literature
| S-EPMC10317201 | biostudies-literature
| S-BSST858 | biostudies-other
| S-EPMC4204447 | biostudies-literature
| S-EPMC8051135 | biostudies-literature
| S-EPMC4664780 | biostudies-literature
| S-EPMC6618293 | biostudies-literature
| S-EPMC2579325 | biostudies-literature